adding passing tests

src/py/crankshaft/crankshaft/space_time_dynamics/markov.py

@@ -54,7 +54,7 @@ def spatial_markov_trend(subquery, time_cols, num_time_per_bin, permutations, ge
     ## rebin time data
     if num_time_per_bin > 1:
         ## rebin
-        t_data = rebin_data(t_data, num_time_per_bin)
+        t_data = rebin_data(t_data, int(num_time_per_bin))
 
     sp_markov_result = ps.Spatial_Markov(t_data, weights, k=7, fixed=False)
 
@@ -68,7 +68,7 @@ def spatial_markov_trend(subquery, time_cols, num_time_per_bin, permutations, ge
     prob_dist = get_prob_dist(lag_classes, sp_markov_result.classes)
 
     ## find the ups and down and overall distribution of each cell
-    trend, trend_up, trend_down, volatility = get_prob_stats(prob_dist)
+    trend_up, trend_down, trend, volatility = get_prob_stats(prob_dist)
 
     ## output the results
 
@@ -127,12 +127,29 @@ def get_prob_dist(transition_matrix, lag_indices, unit_indices):
     return np.array([transition_matrix[(lag_indices[i], unit_indices[i])] for i in range(len(lag_indices))])
 
 def get_prob_stats(prob_dist, unit_indices):
-# trend, trend_up, trend_down, volatility = get_prob_stats(prob_dist)
+    """
+        get the statistics of the probability distributions
 
-    trend_up = np.array([prob_dist[:, i:].sum() for i in unit_indices])
+        Outputs:
-    trend_down = np.array([prob_dist[:, :i].sum() for i in unit_indices])
+            @param trend_up ndarray(float): sum of probabilities for upward
-    trend = trend_up - trend_down
+               movement (relative to the unit index of that prob)
+            @param trend_down ndarray(float): sum of probabilities for downard
+               movement (relative to the unit index of that prob)
+            @param trend ndarray(float): difference of upward and downward
+               movements
+    """
+
+    num_elements = len(prob_dist)
+    trend_up   = np.empty(num_elements)
+    trend_down = np.empty(num_elements)
+    trend      = np.empty(num_elements)
+
+    for i in range(num_elements):
+        trend_up[i] = prob_dist[i, (unit_indices[i]+1):].sum()
+        trend_down[i] = prob_dist[i, :unit_indices[i]].sum()
+        trend[i] = (trend_up[i] - trend_down[i]) / prob_dist[i, unit_indices[i]]
+
+    ## calculate volatility of distribution
     volatility = prob_dist.std(axis=1)
 
-
     return trend_up, trend_down, trend, volatility

src/py/crankshaft/test/test_space_time_dynamics.py

@@ -122,5 +122,28 @@ class SpaceTimeTests(unittest.TestCase):
 
         self.assertTrue(np.array_equal(result, answer))
 
+    def test_get_prob_stats(self):
+        """Test get_prob_stats"""
 
+        probs = np.array([
+            [ 0.0754717 , 0.88207547, 0.04245283, 0.        , 0.        ],
+            [ 0.        , 0.        , 0.09411765, 0.87058824, 0.03529412],
+            [ 0.0049505 , 0.09405941, 0.77722772, 0.11881188, 0.0049505 ],
+            [ 0.        , 0.        , 0.        , 0.02352941, 0.97647059]
+        ])
+        unit_indices = np.array([1, 3, 2, 4])
+        answer_up = np.array([0.04245283, 0.03529412, 0.12376238, 0.])
+        answer_down = np.array([0.0754717, 0.09411765, 0.0990099, 0.02352941])
+        answer_trend = np.array([-0.03301887 / 0.88207547, -0.05882353 / 0.87058824,  0.02475248 / 0.77722772, -0.02352941 / 0.97647059])
+        answer_volatility = np.array([ 0.34221495,  0.33705421,  0.29226542,  0.38834223])
 
+        result = std.get_prob_stats(probs, unit_indices)
+        result_up = result[0]
+        result_down = result[1]
+        result_trend = result[2]
+        result_volatility = result[3]
+
+        self.assertTrue(np.allclose(result_up, answer_up))
+        self.assertTrue(np.allclose(result_down, answer_down))
+        self.assertTrue(np.allclose(result_trend, answer_trend))
+        self.assertTrue(np.allclose(result_volatility, answer_volatility))